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1. Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects [Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects]

   https://doi.org/10.5220/0010225804240431  

   Ray, Aratrika ; Hou, Daqing ; Schuckers, Stephanie ; Barbir, Abbie ( January 2021 , 7th International Conference on Information Systems Security and Privacy)

   Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password, PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse 1 INTRODUCTION Currently smartphones are predominantly protected a patterned password is prone to smudge attacks, and fingerprint scanning is prone to spoof attacks. Other forms of attacks include video capture and shoulder surfing. Given the increasingly important roles smartphones play in e-commerce and other operations where security is crucial, there lies a strong need of continuous authentication mechanisms to complement and enhance one-time authentication such that even if the authentication at the point of login gets compromised, the device is still unobtrusively protected by additional security measures in a continuous fashion. The research community has investigated several continuous authentication mechanisms based on unique human behavioral traits, including typing, swiping, and gait. To this end, we focus on investigating physiological traits. While interacting with hand-held devices, individuals strive to achieve stability and precision. This is because a certain degree of stability is required in order to manipulate and interact successfully with smartphones, while precision is needed for tasks such as touching or tapping a small target on the touch screen (Sitov´a et al., 2015). As a result, to achieve stability and precision, individuals tend to develop their own postural preferences, such as holding a phone with one or both hands, supporting hands on the sides of upper torso and interacting, keeping the phone on the table and typing with the preferred finger, setting the phone on knees while sitting crosslegged and typing, supporting both elbows on chair handles and typing. On the other hand, physiological traits, such as hand-size, grip strength, muscles, age, 424 Ray, A., Hou, D., Schuckers, S. and Barbir, A. Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects. DOI: 10.5220/0010225804240431 In Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 424-431 ISBN: 978-989-758-491-6 Copyrightc 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved still, once compromised, fail to protect the user’s account and data. In contrast, continuous authentication, based on traits of human behavior, can offer additional security measures in the device to authenticate against unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the acceleration and angular velocity data. The best EERs of 2.4% and 6.9% for intra- and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.’s likelihood ratio (LR) based score fusion. 

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2. A privacy‐preserving multifactor authentication system

   https://doi.org/10.1002/spy2.88  

   Acar, Abbas ; Liu, Wenyi ; Beyah, Raheem ; Akkaya, Kemal ; Uluagac, Arif Selcuk ( August 2019 , SECURITY AND PRIVACY)

   In recent years, there has been a significant number of works on the development of multifactor authentication (MFA) systems. Traditionally, behavioral biometrics (eg, keystroke dynamics) have been known to have the best usability because they do not require one to know or possess anything—they simply communicate “how you type” to an authenticator. However, though highly usable, MFA approaches that are based on biometrics are highly intrusive, and users' sensitive information is exposed to untrusted servers. To address this privacy concern, in this paper, we present a privacy‐preserving MFA system for computer users, called PINTA. In PINTA, the second factor is a hybrid behavioral profile user, while the first authentication factor is a password. The hybrid profile of the user includes host‐based and network flow‐based features. Since the features include users' sensitive information, it needs to be protected from untrusted parties. To protect users' sensitive profiles and to handle the varying nature of the user profiles, we adopt two cryptographic methods: Fuzzy hashing and fully homomorphic encryption (FHE). Our results show that PINTA can successfully validate legitimate users and detect impostors. Although the results are promising, the trade‐off for privacy preservation is a slight reduction in performance compared with traditional identity‐based MFA techniques.

    

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3. Use of Auxiliary Classifier Generative Adversarial Network in Touchstroke Authentication

   https://doi.org/10.1109/icmla51294.2020.00049  

   Deb, Debzani ; Guirguis, Mina M. ( December 2020 , 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

   null (Ed.)

   With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast number of synthetic data that effectively simulates an imposter behavior. The use of AC-GAN also diversifies generated samples and stabilizes training. 

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4. Utilizing Keystroke Dynamics as Additional Security Measure to Protect Account Recovery Mechanism [Utilizing Keystroke Dynamics as Additional Security Measure to Protect Account Recovery Mechanism]

   https://doi.org/10.5220/0010191200330042  

   Wahab, Ahmed ; Hou, Daqing ; Schuckers, Stephanie ; Barbir, Abbie ( January 2021 , 7th International Conference on Information Systems Security and Privacy)

   Account recovery is ubiquitous across web applications but circumvents the username/password-based login step. Therefore, it deserves the same level of security as the user authentication process. A common simplistic procedure for account recovery requires that a user enters the same email used during registration, to which a password recovery link or a new username could be sent. Therefore, an impostor with access to a user’s registration email and other credentials can trigger an account recovery session to take over the user’s account. To prevent such attacks, beyond validating the email and other credentials entered by the user, our proposed recovery method utilizes keystroke dynamics to further secure the account recovery mechanism. Keystroke dynamics is a type of behavioral biometrics that uses the analysis of typing rhythm for user authentication. Using a new dataset with over 500,000 keystrokes collected from 44 students and university staff when they fill out an account recovery web form of multiple fields, we have evaluated the performance of five scoring algorithms on individual fields as well as feature-level fusion and weighted-score fusion. We achieve the best EER of 5.47% when keystroke dynamics from individual fields are used, 0% for a feature-level fusion of five fields, and 0% for a weighted-score fusion of seven fields. Our work represents a new kind of keystroke dynamics that we would like to call it ‘medium fixed-text’ as it sits between the conventional (short) fixed text and (long) free text research. 

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5. Continuous authentication using one-class classifiers and their fusion

   https://doi.org/10.1109/ISBA.2018.8311467  

   Kumar, Rajesh ; Kundu, Partha Pratim ; Phoha, Vir V. ( January 2018 , IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA))

   While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (MCC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of MCC. Our findings encourage the research community to use OCC in order to build CAS as it does not require knowledge of impostor class during the enrollment process. 

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